On stable wrapper-based parameter selection method for efficient ANN-based data-driven modeling of turbulent flows

• URL: http://arxiv.org/abs/2308.02602v1
• Date: Fri, 4 Aug 2023 08:26:56 GMT
• Title: On stable wrapper-based parameter selection method for efficient ANN-based data-driven modeling of turbulent flows
• Authors: Hyeongeun Yun, Yongcheol Choi, Youngjae Kim, and Seongwon Kang
• Abstract summary: This study aims to analyze and develop a reduced modeling approach based on artificial neural network (ANN) and wrapper methods. It is found that the gradient-based subset selection to minimize the total derivative loss results in improved consistency-over-trials. For the reduced turbulent Prandtl number model, the gradient-based subset selection improves the prediction in the validation case over the other methods.
• Score: 2.0731505001992323
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: To model complex turbulent flow and heat transfer phenomena, this study aims to analyze and develop a reduced modeling approach based on artificial neural network (ANN) and wrapper methods. This approach has an advantage over other methods such as the correlation-based filter method in terms of removing redundant or irrelevant parameters even under non-linearity among them. As a downside, the overfitting and randomness of ANN training may produce inconsistent subsets over selection trials especially in a higher physical dimension. This study analyzes a few existing ANN-based wrapper methods and develops a revised one based on the gradient-based subset selection indices to minimize the loss in the total derivative or the directional consistency at each elimination step. To examine parameter reduction performance and consistency-over-trials, we apply these methods to a manufactured subset selection problem, modeling of the bubble size in a turbulent bubbly flow, and modeling of the spatially varying turbulent Prandtl number in a duct flow. It is found that the gradient-based subset selection to minimize the total derivative loss results in improved consistency-over-trials compared to the other ANN-based wrapper methods, while removing unnecessary parameters successfully. For the reduced turbulent Prandtl number model, the gradient-based subset selection improves the prediction in the validation case over the other methods. Also, the reduced parameter subsets show a slight increase in the training speed compared to the others.

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